The concept of regression goes back to the mathematician Gauss, who talked about fitting a line through a scatter of points.
回归这个概念要追溯到数学家高斯,讨论的是从若干散点中切合出一条直线
What Gauss did was said, let's fit a line through the point-- the scatter of points--and that's called the regression line.
高斯说,做这样的一条直线,切合所有散点,这就是回归直线
And then I could also do a Gaussian one here, with the mean of and the standard deviation of volatility divided by 2.
然后我在这里再写一个高斯分布的函数,它的浮动值的平均值和,标准偏差值都除了2。
STUDENT: The variance of the Gaussian seems to be less than the variance of the uniform.
学生:高斯分布的变化比,均匀分布的变化小。
But if you think about it, it would not be surprising if the Gaussians, at least, gave us some surprising, more extreme, results, than the uniform.
但是如果你去思考,你不会惊讶高斯分布,会给我们的答案会,比均匀分布大。
And think about, what's the difference between the Gaussian and the normal?
高斯分布和均匀分布的,差别是什么?
He chose the line so that--this is Gauss-- he chose the line to minimize the sum of squared distances of the points from the lines.
高斯选的这条线,所有点距离这条直线的平方和,是所有直线中最小的
The variance of the Gaussian -- STUDENT: is less.
高斯分布的变化-,学生:更小。
A random-- a uniform, and a Gaussian.
一种随机的一种均匀分布和一种高斯分布。
And then we looked at this little loop before, for i in range number of stocks, I'm going to create two different lists of stocks, one where the moves, or distributions, are chosen from a uniform, and the other where they're Gaussian.
这里的这个小循环,因为i代表股票,我会建立两个不同的股票链表,一个是代表股票价格的移动,或者说是分布,它们是从均匀分布中得出的,而另一个链表是从高斯分布中得出的。
It's a famous formula, which is due to Gauss again.
公式如下f =,times e to minus . 这是个很著名的公式,还是来自高斯
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